Calculus & Linear Algebra for Machine Learning from Scratch

Learn Mathematics i.e Calculus & Linear Algebra for Machine Learning, Data Science, Deep Learning, and Big Data.

Ratings 4.17 / 5.00
Calculus & Linear Algebra for Machine Learning from Scratch

What You Will Learn!

  • Functions
  • Differentiation
  • Integration
  • Partial Differentiation
  • Basics of Matrices
  • Addition and Subtraction of Matrices
  • Scalar Multiplication of Matrices
  • Mutiplication of Matrices
  • Vectors
  • Finding Determinant of a Matrix
  • Inverse of a Matrix
  • Eigenvalues and Eigenvectors
  • Linear regression
  • Application of Mathematics in Data Science and Machine Learning

Description

In this course you will learn Calculus and Linear Algebra for Machine Learning and Data Science from scratch.

Calculus and Linear Algebra form the foundation of the essential Mathematical background required in the field of Machine Learning, Deep Learning and Data Science.


In this course you will learn…

Functions

Differentiation

Integration

Partial Differentiation

Basics of Matrices

Addition and Subtraction of matrices

Multiplication of Matrices

Vectors

Eigenvalues and Eigenvectors

Application of Mathematics in Data Science


Calculus & Linear Algebra finds wide variety of applications in different fields of Machine Learning and Data Science. B Learning Calculus & Linear Algebra will help you in understanding advanced topics of Machine Learning and Data Science. If you are taking a Machine Learning or Data Science course, then this course is certainly going to help you.


In this course I have provided video lectures for each and every concept, these lectures are supplemented by quizzes and examples that will help you reinforcing your knowledge and will also help you in learning the Math behind Machine Learning.

Who Should Attend!

  • Beginner Data Science Students
  • Students facing difficulty in understanding Mathematical prerequisites of Python and Data Science

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Tags

  • Linear Algebra

Subscribers

223

Lectures

38

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